1. Field of the Invention
The present invention relates to a method of determining weights of image features by considering both the recent user relevance feedback information and the whole feedback information in a system which determines the weights of the image features using the user relevance feedback, and a data structure therefor.
In particular, the present invention relates to a multimedia data structure and a method of determining weights of image features using the multimedia data structure which can reflect both the recent user relevance feedback information determined for a predetermined time period or by the predetermined number of frequency and the information on the whole user relevance feedback information till now when determining the weights of the image features in a multimedia search system which changes the weights of the image features based on the user relevance feedback if the user gives the feedback regarding a similarity with respect to a certain image and then uses the changed weights of the image features for the future search.
2. Description of the Related Art
Conventionally, in measuring the similarity between images for an effective image search, a method of determining the similarity by giving a weight for each feature, and a method of determining the similarity by giving a weight for each element in a feature are used.
Specifically, if the user gives feedback information on a similar image or an image not similar as he/she views images presented in response to a user's query, the system automatically calculates weights of respective features such as color histogram, texture histogram, dominant color, etc., using the feedback information, and uses the calculated weights for the image search.
Such learned weights may be stored in table, and used again for subsequent searches for other images.
Also, in case of searching an image having no weight given thereto, a previously learned weight of another similar type image may be used. For this, similar image group information is managed in a table.
Specifically, even when the current reference image has no weight given thereto, a search is performed with reference to a specified weight if any other image in the group, to which the reference image belongs, has such a weight.
In the image search reflecting the user relevance feedback as described above, the method using the learned weight through the user relevance feedback has an advantage that the weight is the value which reflects the whole feedback till now with respect to the image, but has a disadvantage that it cannot reflect the change of the recent feedback pattern when the recent user feedback pattern is changed.
For instance, if the corresponding image moves to another database, a feedback different from the feedback pattern that the user has reflected till now will be performed.
In this case, if lots of feedback are reflected as a predetermined time elapses after the database movement, the feedback pattern produced after the database movement will affect as more important information than the previously reflected feedback pattern.
However, the above-described weight learning method does not provide the result learned using only the feedback within a specified period, and thus it can hardly reflect the user feedback pattern recently changed in case that it has only the weight learning information according to the whole feedback as described above.
Meanwhile, in case of managing the similar image group information in list, all the feedback images cannot be managed in list since a large amount of storage space is required, and thus a limited list using only the recent feedback information can be managed.
In this case, the recent user feedback pattern can be reflected, but the whole feedback pattern cannot be reflected.
For instance, if the weight of the image feature is determined and learned only by the limited feedback for the latest period in the event that the recent user feedback is not in consistency, the characteristics of the corresponding image cannot be reflected, and the reliability of the image feature information greatly deteriorates due to the irregular feature weight learning.